Hi all,
Currently, I am doing a research in classifying RBCs into four groups.
After extracting best features, I trained a pattern recognition Neural Network and performance is evaluated by 10-fold cross validation check. Miss-classification rate is around 6%.
In another experiment, I tried to use SVM model to classify one group and three other groups so called one-versus-rest classifiers. Then again repeated same strategy for the 3-group class until there is no need for another binary classification. Performance is evaluated by the same technique applied in the Neural Network strategy. Miss-classification rate for the first binary SVM classifier is zero and for the second and third binary SVM classifier is 2.1% and 3.2%. Which strategy is better and why?
If multi stage SVM is outperforming NN strategy, how can I evaluate the miss-classification rate?
Cheers,
What are RBCs?
The first thing that i always ask with multiclass problems is the class distribution. If the classes are unbalanced accuracy isn't a great metric for performance evaluation - try something like f-measure instead.
There's a commonly stated 'no free lunch' theorem that, loosely says, no optimisation algorithm is always better than another. So, for an arbitrary dataset like I will assume yours is, there isn't an answer to your question :-)
Generally, these questions are either answered empirically, or you may be able to specify a sensible model apriori by understanding the data and the problem.
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